data.mim: Generate a clinical trial data set under a specified multiple...
In anoint: Analysis of Interactions

Description Usage Arguments Details Value Author(s) Examples

Returns a simulated equal-allocation, parallel-group clinical trial with possibly multiple interactions among prognostic factors.

1 2	data.anoint(alpha, beta, gamma, mean, vcov, n=100, event=.8, type = c("binomial","survival"))

`alpha`	vector specifying control and treatment group intercept
`beta`	vector specifying prognostic factor main effects
`gamma`	vector specifying modification of prognostic effects in the presence of treatment
`mean`	vector of covariate mean to be supplied to `mvrnorm` of the `MASS` package.
`vcov`	matrix of variance-covariance matrix of prognostic covariates to be supplied to `mvrnorm`
`n`	number of subjects in each treatment arm
`event`	proportion of observed events when `type` is "survival"
`type`	string indicating type of response variable

When type is "survival", the parameters specify the log-rate for an exponentially distributed random variable. Censored times are non-informatively right-censored. When type is "binomial", the parameters specify a log-odds model.

Data frame with y, trt, and V1,...,VK prognostic factors. The data frame also has event with a time-to-event reponse, which is an indicator for an observed event.

S. Kovalchik s.a.kovalchik@gmail.com

null.interaction <- data.anoint(
                             alpha = c(log(.2/.8),log(.2*.75/(1-.2*.75))),
                             beta = c(1.5,2),
                             gamma = rep(1,2),
                             mean = c(0,0),
                             vcov = diag(2),
                             type="binomial"
                             )

fit <- glm(y~(.)*trt,data=null.interaction,family="binomial")

summary(fit)


null.interaction <- data.anoint(
                             alpha = c(log(.5),log(.5*.75)),
                             beta = c(1.5,2),
                             gamma = rep(1,2),
                             mean = c(0,0),
                             vcov = diag(2),
                             type="survival",
                             event = .7
                             )

fit <- coxph(Surv(y, event)~(.)*trt,data=null.interaction)

summary(fit)